CLTestCheck: Measuring Test Effectiveness for GPU Kernels


Massive parallelism, and energy efficiency of GPUs, along with advances in their programmability with OpenCL and CUDA programming models have made them attractive for general-purpose computations across many application domains. Techniques for testing GPU kernels have emerged recently to aid the construction of correct GPU software. However, there exists no means of measuring quality and effectiveness of tests developed for GPU kernels. Traditional coverage criteria over CPU programs is not adequate over GPU kernels as it uses a completely different programming model and the faults encountered may be specific to the GPU architecture. GPUs have SIMT (single instruction, multiple thread) execution model that executes batches of threads (work groups) in lock-step, i.e all threads in a work group execute the same instruction but on different data.

We address this need in this paper and present a framework, CLTestCheck, for assessing quality of test suites developed for OpenCL kernels. The framework has the following capabilities, 1. Measures kernel code coverage using three different coverage metrics that are inspired by faults found in real kernel code, 2. Seeds different types of faults in kernel code and measures fault finding capability of test suite, 3. Simulates different work group schedules to check for potential data races with the given test suite. We conducted empirical evaluation of CLTestCheck on a collection of 82 publicly available GPU kernels and test suites. We found that CLTestCheck is capable of automatically measuring effectiveness of test suites, in terms of kernel code coverage, fault finding and revealing data races in real OpenCL kernels.

In proceedings of International Conference on Fundamental Approaches to Software Engineering (FASE 2019, ETAPS 2019)
Chao Peng
Chao Peng
Senior Researcher

My research interests include Software Testing, Program Repair and Compilers.